def main():
"""Aggregates the outputs of all the tools for a specific dataset.
For command line help, run with the '-h' flag.
"""
args = get_args()
dataset, chrom, _ = common.process_args(args)
if not dataset: return
# Collects the scores to the scored_targets dictionary, where the first ket
# is the target spacer, the second is the tool and the third is the score's
# name.
targets = dataset.get_targets(args.chr)
scored_targets = {}
for target in targets:
scored_targets[get_spacer(target)] = defaultdict(defaultdict)
for tool in TOOLS.values():
add_scores(args, tool, scored_targets)
# Converts the mapping to a list of records, each one containing all of the
# scores of its corresponding target.
records = aggregate(targets, scored_targets)
# Writes the output.
aggregate_path = join_path(dataset.get_out_path(),
consts.AGG_OUT_NAME % args.chr)
with open(aggregate_path, 'w') as out:
out.write(common.to_csv_line(*get_headers()))
for record in records:
out.write(common.to_csv_line(*record))
def add_rejected(chrom, out_path):
"""Adds the rejected targets to the output, with the rejection error codes.
Args:
chrom: A Chromosome instance.
out_path: Path to the directory of the tool's output for the processed
dataset.
Writes:
The complete output CSV file.
"""
# Collects rejected spacers with their rejection reasons.
rejections = []
rejection_file_path = os.path.join(out_path, REJECT_FILE_NAME % chrom.num)
with open(rejection_file_path, 'r') as fd:
for line in fd:
seq, reason = line.strip().split('\t')
if reason not in REJECTIONS:
# Stops if there is an unknown rejection reason.
print "Unknown rejection reason: %s" % reason
return
rejections.append((seq[:-consts.PAM_LEN], REJECTIONS[reason]))
# Adds the rejected targets to the output file.
out_file_path = os.path.join(out_path, consts.RAW_OUT_CSV % chrom.num)
with open(out_file_path, 'a') as fd:
for seq, reason in rejections:
line = common.to_csv_line(seq, "", "", "", reason, chrom.name, -1,
-1, "x", "", "", "", "")
fd.write(line)
def main(raw_args=None):
"""Samples targets from the GenomeCRISPR DB into a CSV dataset file.
For command line help, run with the '-h' flag.
Writes:
A dataset CSV file with the sampled targets.
"""
args = get_args(raw_args)
dataset = DATASETS[args.dataset]
dataset.set_work_dir(args.path)
dataset_dir = os.path.join(dataset.get_data_path(), consts.RAW_DATA_DIR)
input_file = os.path.join(dataset_dir, args.input)
if args.output:
out_name = args.output
else:
out_name = OUT_FILE % (args.chr, format_number(args.size))
output_file = os.path.join(dataset_dir, out_name)
print("Output will be in: %s" % output_file)
entries = set()
out = open(output_file, 'w')
input = open(input_file, 'r')
is_first = True
for line in input:
if is_first:
add_headers(line.strip(), out)
is_first = False
else:
entries.add(line)
input.close()
print("Done reading")
# Keeps track of spacers that were already included.
spacers = set()
# Keeps track of the number of samples already obtained.
sampled = 0
# Keeps track of progress for printing.
chunk = 1
# While we have not sampled enough...
while sampled < args.size:
remaining = args.size - sampled
# Sample more entries.
new_entries = sample(entries, spacers, remaining, dataset)
sampled += len(new_entries)
for entry in new_entries:
out.write(common.to_csv_line(*entry))
# If we have passed a progress checkpoint - prints progress.
if (args.size / PRINT_NUM) * chunk < sampled:
print("Sampled: %d" % sampled)
chunk += 1
print("Done")
print(STATS)
def add_headers(headers, out):
"""Writes the DB headers along with an efficiency header to the output file.
Args:
headers: The existing headers in the GenomeCRISPR DB.
out: A file descriptor for the output file.
"""
out.write(common.to_csv_line(headers, "efficient"))
def parse_features(path, handlers, fd_out, label_getter):
"""Parses the input to produce the feature representations.
Args:
path: Path to the input.
handlers: A list of encoding functions for the columns of the input.
fd_out: A file descriptor for the output file.
label_getter: A function which gets the GenomeCRISPR effect label for a
given target.
"""
with open(path, 'r') as fd:
lines = fd.readlines()
for line in lines[1:]:
values = common.from_csv_line(line)
target = values[0]
encoded = list(chain(*[handlers[i](values[i+1])\
for i in range(len(handlers))]))
entry = [target, label_getter(target)] + encoded
fd_out.write(common.to_csv_line(*entry))
def to_csv_line(self, *args):
"""Returns a CSV line with the args, prefixed by the tool's name."""
return common.to_csv_line(self.name, *args)